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08/02/2023
Tango: rethinking quantization for graph neural network training on GPUs
Graph Neural Networks (GNNs) are becoming increasingly popular due to th...
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07/14/2023
DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training
Memory-based Temporal Graph Neural Networks are powerful tools in dynami...
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06/05/2023
Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications
Model pre-training on large text corpora has been demonstrated effective...
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04/20/2023
Train Your Own GNN Teacher: Graph-Aware Distillation on Textual Graphs
How can we learn effective node representations on textual graphs? Graph...
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02/24/2023
PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction
Transparency and accountability have become major concerns for black-box...
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01/16/2023
PIGEON: Optimizing CUDA Code Generator for End-to-End Training and Inference of Relational Graph Neural Networks
Relational graph neural networks (RGNNs) are graph neural networks (GNNs...
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09/24/2022
From Local to Global: Spectral-Inspired Graph Neural Networks
Graph Neural Networks (GNNs) are powerful deep learning methods for Non-...
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06/22/2022
Efficient and effective training of language and graph neural network models
Can we combine heterogenous graph structure with text to learn high-qual...
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06/21/2022
Nimble GNN Embedding with Tensor-Train Decomposition
This paper describes a new method for representing embedding tables of g...
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03/28/2022
TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs
Many real world graphs contain time domain information. Temporal Graph N...
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12/31/2021
Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Graphs
Graph neural networks (GNN) have shown great success in learning from gr...
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09/16/2021
Dr. Top-k: Delegate-Centric Top-k on GPUs
Recent top-k computation efforts explore the possibility of revising var...
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06/11/2021
Global Neighbor Sampling for Mixed CPU-GPU Training on Giant Graphs
Graph neural networks (GNNs) are powerful tools for learning from graph ...
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05/03/2021
Schema-Aware Deep Graph Convolutional Networks for Heterogeneous Graphs
Graph convolutional network (GCN) based approaches have achieved signifi...
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01/19/2021
Learning over Families of Sets – Hypergraph Representation Learning for Higher Order Tasks
Graph representation learning has made major strides over the past decad...
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10/11/2020
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs
Graph neural networks (GNN) have shown great success in learning from gr...
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08/26/2020
FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems
Graph neural networks (GNNs) are gaining increasing popularity as a prom...
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07/20/2020
PanRep: Universal node embeddings for heterogeneous graphs
Learning unsupervised node embeddings facilitates several downstream tas...
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07/20/2020
Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing
Predicting interactions among heterogenous graph structured data has num...
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04/18/2020
DGL-KE: Training Knowledge Graph Embeddings at Scale
Knowledge graphs have emerged as a key abstraction for organizing inform...
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07/07/2019
Graphyti: A Semi-External Memory Graph Library for FlashGraph
Graph datasets exceed the in-memory capacity of most standalone machines...
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02/24/2019
clusterNOR: A NUMA-Optimized Clustering Framework
Clustering algorithms are iterative and have complex data access pattern...
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04/25/2018
Challenges Towards Deploying Data Intensive Scientific Applications on Extreme Heterogeneity Supercomputers
Shrinking transistors, which powered the advancement of computing in the...
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09/05/2017
Linear Optimal Low Rank Projection for High-Dimensional Multi-Class Data
Classification of individual samples into one or more categories is crit...
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02/03/2016
An SSD-based eigensolver for spectral analysis on billion-node graphs
Many eigensolvers such as ARPACK and Anasazi have been developed to comp...
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06/24/2015
